Recently, deep learning-based methods have dominated the state-of-the-art performance on the widely adopted 2D gait recognition benchmarks like CASIA-B [1]} and OU-MVLP [2]} by directly learning discriminative features from silhouette sequences [3]}, [4]}, [5]} or GEIs [6]}.
Despite the excellent results on the in-the-lab datasets, these methods cannot work well in the wild scenarios which have more diverse 3D viewpoints of cameras and more complex environmental interference factors like occlusions [7]}.
Although several works exploit 3D cylinders [8]} or 3D skeletons [9]}, these sparse 3D models also lose helpful information of human bodies like viewpoints and shapes.
Fortunately, the development of parameterized human body models like the Skinned Multi-Person Linear (SMPL) model [10]} and 3D human mesh recovery approaches [11]}, [12]}, [13]} makes it possible to estimate precise 3D meshes and viewpoints of human bodies in video frames.
The advantages of 3D meshes for gait recognition are two-fold:
1) the 3D mesh can provide not only the pose but also the shape of the human body in the 3D space, which is crucial for learning discriminative features of gait, and
2) the 3D viewpoint can be explored to normalize the orientations of human bodies during cross-view matching.
